About healthiar 1/2

healthiar offers different calculation pathways and options

DALY = disability-adjusted life years; YLD = years lived with disability; YLL = years of life lost

About healthiar 2/2

Selection of healthiar core family members (functions)

• attribute_health() impacts to environmental exposure
• summarize_uncertainty() via Monte Carlo simulation
• compare() two scenarios
• monetize() health impacts
• …

Refresher - Burden of disease with relative risk

R function call

attribute_health() with RR

Goal: attribute COPD cases to PM2.5 air pollution exposure

results_pm_copd <- attribute_health(
  erf_shape = "log_linear", # erf = exposure-response function
  rr_central = 1.369, 
  rr_increment = 10,  # μg / m^3
  exp_central = 8.85, # μg / m^3
  cutoff_central = 5, # μg / m^3
  bhd_central = 30747 # bhd = baseline health data (here: COPD incidence)
) 

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = exdat_pm_copd$relative_risk, 
  rr_lower = exdat_pm_copd$relative_risk_lower,
  rr_upper = exdat_pm_copd$relative_risk_upper,
  rr_increment = 10, 
  exp_central = exdat_pm_copd$mean_concentration,
  cutoff_central = exdat_pm_copd$cut_off_value,
  bhd_central = exdat_pm_copd$incidence
) 

Main results

results_pm_copd$health_main

Detailed results

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369, 
  rr_lower = 1.114, 
  rr_upper = 1.664,
  rr_increment = 10,
  exp_central = 8.85, 
  exp_lower = 8, 
  exp_upper = 10,
  cutoff_central = 5,
  bhd_central = 30747, 
  bhd_lower = 28000, 
  bhd_upper = 32000
) 

results_pm_copd$health_detailed$results_raw

attribute_health() with user-defined ERF

Goal: attribute COPD cases to air pollution exposure by applying a user-defined exposure response function (e.g. MR-BRT curves from Global Burden of Disease study)

• Any function of the form intercept + a x c^1 + b x c^2 + …

• Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()

results_pm_copd_mr_brt <- attribute_health(
  exp_central = 8.85,
  bhd_central = 30747,
  erf_eq_central = splinefun(
    x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
    y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
    method = "natural")
)

Main results

results_pm_copd_mr_brt$health_main

Refresher - Burden of disease with absolute risk

attribute_health() with AR

Goal: attribute cases of high annoyance (HA) to noise exposure

results_noise_ha <- attribute_health(
  approach_risk = "absolute_risk",
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2",
  exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
  pop_exp = c(387500, 286000, 191800, 72200, 7700),
)

Main results

results_noise_ha$health_main

Detailed results

results_noise_ha$health_detailed$results_raw

attribute_health() across multiple geographic units

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

results_iteration <- attribute_health(
  geo_id_disaggregated = c("Zürich", "Basel", "Geneva", "Ticino", "Jura"), 
  geo_id_aggregated = c("German","German","French","Italian","French"),
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_increment = 10, 
  cutoff_central = 5,
  exp_central = c(11, 11, 10, 8, 7),
  bhd_central = c(4000, 2500, 3000, 1500, 500)
)

Main results

results_iteration$health_main

Detailed results

results_iteration$health_detailed$results_raw

Main results

results_pm_copd$uncertainty_main

Detailed results

results_pm_copd$uncertainty_detailed$attribute_by_sim_disaggregated

Comparison results

results_comparison$health_main

monetize() health impacts

Goal: monetize (attributable) health impacts (obtained from healthiar or other source)

• Discounting yes/no
• Inflation yes/no
• Stand-alone use either monetize impact from healthiar or other source

results_monetization <- monetize(
  output_attribute = results_pm_copd,
  valuation = 20,
  discount_shape = "exponential",
  discount_rate = 0.03,
  discount_years = 5
)

Main monetization results

results_monetization$monetization_main

Other healthiar functions

• attribute_lifetable() for YLL
• attribute_health for YLD (using the dw & duration arguments)
• get_daly as the sum of YLL and YLD
• cba() cost-benefit analysis
• multiexpose() includes multiple exposures (e.g. PM2.5 & NO2) in your assessment
• prepare_exposure() calculates (population-weighted) mean exposure by combining spatial exposure geographic units data
• socialize() determines burden attributable to differences in a social indicator
  • E.g. share of health impacts due to living in a poor neighborhood (with higher exposure) instead of a rich neighborhood (with lower exposure)
• attribute_mod() modifies an existing attribute_health() assessment

healthiar in RStudio 1/3

Figure: Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio 2/3

Figure: Intro to healthiar vignette (= detailed guide to the package)

healthiar in RStudio 3/3

Figure: Help page of attribute_health()

Outlook 1/2

• healthiar will be publicly available by the end of the year
  • Contact us in case you need early access
  • healthiar might be expanded in the future (if funding available)
• Python version coming out later in 2026
• Two international workshops planned later this year (see next slide)

Outlook 2/2

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

openxlsx::write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results